Model last updated at 2022-06-09 18:16:41.

Model Overview

The details are given in [@slingsby_near-real_2020;@wilson_climatic_2015], but in short what we do is estimate the age of a site by calculating the years since the last fire. We then fit a curve to model the recovery of vegetation (measured using NDVI) as a function of it’s age. For this we use a negative exponential curve with the following form:

\[\mu_{i,t}=\alpha_i+\gamma_i\Big(1-e^{-\frac{age_{i,t}}{\lambda_i}}\Big)\]

where \(\mu_{i,t}\) is the expected NDVI for site \(i\) at time \(t\)

The observed greenness \(NDVI_{i,t}\) is assumed to follow a normal distribution with mean \(\mu_{i,t}\) \[NDVI_{i,t}\sim\mathcal{N}(\mu_{i,t},\sigma_)\]

An additional level models the parameters of the negative exponential curve as a function of environmental variables. This means that sites with similar environmental conditions should have similar recovery curves. The full model also includes a sinusoidal term to capture seasonal variation, but lets keep it simple here.

Workflow

This repository was developed using the Targets framework as follows.

Results

Environmental Controls on Ecosystem Recovery

These parameters represent the relationship of the following environmental variables to the recovery trajectory.

Recovery Trajectories

The plot below illustrates some example recovery trajectories. It currently just shows the top 20 cells with the most observations.

Spatial Predictions

Maps of spatial parameters in the model.

Park-specific Information

NDVI and Time Since Fire

Figure X. Leaflet map showing NDVI and the number of years since the most recent fire. NA values in the NDVI layer indicate missing data (e.g. due to clouds, water, etc.). NA values in the fire layer indicate that no fires have been recorded for that area.